An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

An experimental study on the deformation behaviour and fracture mode of recycled aluminium alloy AA6061-reinforced alumina oxide undergoing high-velocity impact

The anisotropic behaviour and the damage evolution of recycled aluminium alloy-reinforced alumina oxide are investigated in this paper using Taylor impact test. The test is performed at various impact velocity ranging from 190 to 360 m/s by firing a cylindrical projectile towards anvil target. The deformation behaviour and the fracture modes are analysed using the digitized footprint of the deformed specimens. The damage initiation and the progression are observed around the impact surface and the surface 0.5 cm from the impact area using the scanning electron microscope. The deformed specimens showed several ductile fracture modes of mushrooming, tensile splitting and petalling. The critical impact velocity is defined below 280 m/s. The specimens showed a strong strain-rate dependency due to the damage evolution that is driven by severe localized plastic-strain deformation. The scanning electron microscope analysis showed the damage mechanism progress via voids initiation, growth and coalescence in the material. The micrograph within the footprint surface shows the presence of alumina oxide particles within the specimen. The microstructure analysis shows a significant refinement of the specimen particle at the surface located 0.5 cm above the impact area. ImageJ software is adopted in this work to measure the average size of voids within this surface. Non-symmetrical (ellipse-shaped) footprint around the footprints showed plastic anisotropic behaviour. The results in this paper provide a better understanding of the deformation behaviour of recycled materials subjected to dynamic loading. This information on mechanical response is crucial before any potential application can be established to substitute the primary sources

Patterns Identification of Finger Outer Knuckles by Utilizing Local Directional Number

Finger Outer Knuckle (FOK) is a distinctive biometric that has grown in popularity recently. This results from its inborn qualities such as stability, protection, and specific anatomical patterns. Applications for the identification of FOK patterns include forensic investigations, access control systems, and personal identity. In this study, we suggest a method for identifying FOK patterns using Local Directional Number (LDN) codes produced from gradient-based compass masks. For the FOK pattern matching, the suggested method uses two asymmetric masks—Kirsch and Gaussian derivative—to compute the edge response and extract LDN codes. To calculate edge response on the pattern, an asymmetric compass mask made from the Gaussian derivative mask is created by rotating the Kirsch mask by 45 degrees to provide edge response in eight distinct directions. The edge response of each mask and the combination of dominating vector numbers are examined during the LDN code-generating process. A distance metric can be used to compare the LDN code\u27s condensed representation of the FOK pattern to the original for matching purposes. On the Indian Institute of Technology Delhi Finger Knuckle (IITDFK) database, the efficiency of the suggested procedure is assessed. The data show that the suggested strategy is effective, with an Equal Error Rate (EER) of 10.78%. This value performs better than other EER values when compared to different approaches

Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification

The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used to extract the features, which combines the usage of Haar-like features with the application of Histogram of Oriented Gradients (HOG).The SVM classifier is then fed the characteristics it has extracted from the pictures in order to make an accurate classification. A false rejection error rate of 8% is achieved while the accuracy of hand gesture detection is improved by 93.5%

Bio : A Mulrimodal biometric authentication system for person identification and verification

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Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

The new hand geometry system and automatic identification

For the past decades an extensive amount of time and effort has been consumed for the research and development of biometric-based recognition systems. One such system is the one that can recognize based on hand geometry. The objective of this thesis is to explore the usage of hand geometry for developing a hand geometry recognition system. This paper proposes a system performing automatic recognition without the use of specific hardware. The system emphases on executing feature extractions from a typical database and then developing a neural network classifier based on back-propagation architectures with various exercise methods. Features are dug out by the use of morphological (segmentation) operation. Our Experiments were carried out on 500 images (50 persons, 10 images each) under distinctive conditions with possible deliberation of scaling, Translation, Rotation, Color and Illumination modification. The accurate recognition rate is about 96.41 % for the matching of artificial neural network which is calculated by the formula average of sum of errors divided over the number of images